
import pandas as pd
import plotly.express as px
from IPython.display import display
from typing import List
px.set_mapbox_access_token(open(".mapbox_token").read())
pd.options.display.max_rows = 10
from helpers import  _load_synthetic_clinics, _load_at_risk_zip3, _k_closest_clinics, draw_status_treemap, _load_zip3_census, draw_at_risk_vs_clinic_locations_map

draw_status_treemap()


def build_distance_matrix(origin_zip3: List[str], clinics: pd.DataFrame, k:int =10) -> pd.DataFrame:
    #  this takes 2 min at 500 clinics / could consider optimizing
    """
        Iterates through each zip3 <> clinic permutation, 
        and selects the min(distance) k clinic locations
    """
    dist_matrix = pd.concat(
        [_k_closest_clinics(zip3, clinics, k=k) for zip3 in origin_zip3]
    ).reset_index(drop=True)
    return dist_matrix

at_risk = _load_at_risk_zip3(adi_floor=20) # I could use alternate perspective on where to tune to
clinics = _load_synthetic_clinics(n=500) # see helpers.py for "Why Synthetic"

at_risk_distances = build_distance_matrix(at_risk['_zip3'], clinics)
at_risk_distances

Finding distances to 10 closest clinics for each of 572 zip3 origin locations with ADI above 20


def _get_at_risk_stats(at_risk_distances: pd.DataFrame) -> pd.DataFrame:
    at_risk_stats = at_risk_distances.groupby(['_origin_zip3']).agg(
        k=("_distance","count"),
        distance_mean=("_distance","mean"), # mean distance to closest k clinics
        distance_min=("_distance","min"),
        distance_max=("_distance","max")
    ).reset_index()

    def _geocode_zip3(at_risk_stats):
        return at_risk_stats.rename(columns={"_origin_zip3":"_zip3"}).merge(_load_zip3_census()) #somethings off with _state)
    
    at_risk_stats = _geocode_zip3(at_risk_stats) 
    return at_risk_stats
at_risk_stats = _get_at_risk_stats(at_risk_distances)
at_risk_stats = at_risk_stats[at_risk_stats['_state'] != "NY"] # hmmm something messed up with NY and PA upstream
at_risk_stats


px.box(
    at_risk_stats, x="_state", y="distance_mean", 
    title="Range of Mean Distance to 10 Closest Clinics | by State"
).show(renderer="notebook")


print(at_risk_stats['_adi_mean'].mean())
px.scatter_mapbox(
    at_risk_stats, lat="_lat", lon="_lng", size_max=15, hover_data=['_adi_mean'],
    height=800, zoom=3, size='_census_total', color='distance_mean', 
    title=f"Unprotected Origin Locations | n={len(at_risk_stats)} Zip3 | Color=avg distance to 10 closest clinics | mean={int(at_risk_stats['distance_mean'].mean())} miles"
).show(renderer='notebook')

67.58602319455363


px.histogram(
    at_risk_stats, x='distance_mean',
    title=f"Median miles protection seeker would need to travel: {at_risk_stats['distance_mean'].median()} miles | from n={len(at_risk_stats)} origins"
).show(renderer="notebook")


# def draw_at_risk_vs_clinic_locations_map() -> None:
    
#     """
#         Illustrative of Areas with Protections, vs those without
#         Why syntethic clinics
#     """
#     at_risk = _load_at_risk_zip3()
#     clinics = _load_synthetic_clinics(n=500)
#     locations = pd.concat([clinics,at_risk]).fillna(at_risk['_census_total'].mean())
#     px.scatter_mapbox(
#         locations, lat="_lat", lon="_lng", size_max=15, mapbox_style="open-street-map",
#         height=700, zoom=3, color='_state', hover_data=['_state','_zip3'],
#         size='_census_total',
#         title=f"At Risk Areas (3-Digit Zipcode) vs (Synthetic) Clinic Locations | Scaled by Population"
#     ).show(renderer='notebook')
#     return locations

# draw_at_risk_vs_clinic_locations_map()


def draw_closest_clinics_by_state(states: list):
    at_risk = _load_at_risk_zip3()
    _at_risk = at_risk[at_risk['_state'].isin(states)]
    locations = pd.concat([_at_risk, _load_synthetic_clinics()])

    px.scatter_mapbox(
        locations, lat="_lat", lon="_lng", size_max=15, 
        height=600, zoom=3, color='_type', 
        title=f"At Risk Areas vs (Simulated) 10 Closest Clinic Locations"
    ).show(renderer='notebook')

draw_closest_clinics_by_state(["PA",'LA'])

Finding distances to 10 closest clinics for each of 498 zip3 origin locations with ADI above 50

	_state	_clinic_zip5	_lat	_lng	_type	_origin_zip3	_distance
0	IL	62963	37.094559	-89.169921	synthetic_clinic	301**	290
1	IL	62906	37.462889	-89.177551	synthetic_clinic	301**	307
2	IL	62820	38.248629	-88.235358	synthetic_clinic	301**	316
3	IL	62862	38.219115	-88.299337	synthetic_clinic	301**	317
4	IL	62932	37.892867	-89.213656	synthetic_clinic	301**	329
...	...	...	...	...	...	...	...
5715	CO	80017	39.699438	-104.775296	synthetic_clinic	828**	362
5716	CO	81652	39.466243	-107.673259	synthetic_clinic	828**	364
5717	CO	80127	39.530726	-105.164344	synthetic_clinic	828**	368
5718	CO	80108	39.444376	-104.853179	synthetic_clinic	828**	378
5719	CO	81251	39.142548	-106.437579	synthetic_clinic	828**	385

	_zip3	k	distance_mean	distance_min	distance_max	_state	_lat	_lng	_census_total	_adi_mean
1	127**	10	42.9	26	54	PA	41.485554	-74.892326	1207	47.000000
3	147**	10	54.5	28	74	PA	42.046328	-79.669830	2540	75.000000
4	150**	10	155.2	100	187	PA	40.457488	-80.065227	448172	65.563380
5	151**	10	150.8	92	180	PA	40.419911	-79.925283	365418	70.925926
6	152**	10	152.5	96	183	PA	40.445441	-79.979009	687276	63.170732
...	...	...	...	...	...	...	...	...	...	...
638	979**	10	207.2	0	253	OR	43.801827	-117.543657	31751	63.200000
639	990**	10	116.0	19	197	ID	47.635828	-117.094140	6264	41.000000
640	990**	10	116.0	19	197	WA	47.619646	-117.441078	142008	50.387097
641	991**	10	105.8	14	184	ID	47.097949	-117.081329	321	62.000000
642	991**	10	105.8	14	184	WA	47.840974	-118.012785	115238	62.625000

NB: a good goal here is to find a way to strategically deploy resources to minimize harm¶

_archive¶

Simulation: Distance Traveled¶